Novel design of an effective pneumatic magnetostrictive patch transducer based on the ultrasonic guided wave for application of fast pipe health inspection

Hou, Edison Z.Y.; Rostami, Javad

doi:10.1088/1361-6501/ac0a0d

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Zenghua Liu et al [15] used the distributed planar solenoid originally used in the pipe test to replace the axially wrapped solenoid to provide a dynamic magnetic field. Edison et al [16] combined the air pump and magnetostrictive patch transducer for fast pipe health inspection. Zhang et al [17] designed a helical comb MPT for excitation of various orders of torsional GW to examine different types of defects in a pipe.…”

Section: Introductionmentioning

confidence: 99%

Performance investigation on the balance of static and dynamic magnetic field strength of magnetostrictive patch transducer with different permanent magnets

Hou

Rostami

et al. 2022

J. Inst.

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Nowadays, a magnetostrictive patch transducer(MPT) utilizing the high magnetostriction patch such as iron-cobalt alloy attached to the tested specimen like pipes, tubes, and cables has attracted more attention from many scholars. A harmonized flexible printed coil magnetostrictive patch transducer (HFPC-MPT) was proposed in 2019 by the author's research group, and this high-performance MPT could generate a more even magnetostrictive force than before. However, that research was an MPT optimization from sensor circuit design perspective. From the perspective of the magnetic field, this type of sensor has not been explored yet to improve its performance. In this research, firstly, the author conducted numerical simulations to find the optimal balance range between the static and dynamic magnetic fields of HFPC-MPT. Secondly, some experiments have been designed to verify the simulation results. The simulation results have been validated and are in agreement with the experimental results. This research provided a deep insight into improving the performance of HFPC-MPT from the perspective of the static magnetic field, which is also beneficial to both industrial and academic areas.

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Section: Introductionmentioning

confidence: 99%

Performance investigation on the balance of static and dynamic magnetic field strength of magnetostrictive patch transducer with different permanent magnets

Hou

Rostami

et al. 2022

J. Inst.

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“…Bang et al applied time-frequency-domain reflectometry, originally used for electrical cable diagnostics, to guided wave testing of pipes with improved results [5]. One magnetostrictive patch transducer based on guided wave was proposed to identify the axial location of pipeline defects [6]. Deep residual neural network combined with guided wave was used to implement defect classification [7].…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network

Lv,

Chen,

Tong

et al. 2023

Meas. Sci. Technol.

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Pipeline corrosion quantification plays a vital role in guaranteeing the safety of critical industrial structures and thus significant work has been carried out to address such an issue. Although quantitative imaging is crucial for non-destructive testing, research in guided wave pipeline testing has primarily centered on qualitative approaches. Here, we propose a deep neural network built upon physical model to reconstruct pipe wall thickness from ultrasonic guided wave (UGW) signals. The workflow of reconstruction contains three layers, where each layer consists of a fixed forward network and a residual inversion network. The forward model is represented by an agent convolutional neural network which would be embedded into the entire inversion network. The residuals between data from the forward model and real signals are then mapped into velocity profile differences through sub-inversion network. Numerical experiments were conducted to verify the inversion performance of the deep neural network using thickness maps obtained from guided wave frequency domain information. Results show that inversion images are capable to reveal the positions, shapes, and depths of corrosion with high resolution and precision, yielding an average inversion of 87.37% in the test set. In addition, by utilizing the periodicity of the pipeline, the inversion accuracy of eight pairs of transducers were improved from 67.7% to 89.43% with high-order helical guided wave. Compared with traditional high-precision inversion methods such as full waveform inversion, the proposed method achieved approximately 300 times faster inversion speed at the cost of some accuracy. The research demonstrates that real-time quantitative imaging of defects on pipes can be achieved accurately by physics embedded network. Furthermore, an experimental verification of the method was carried out through UGW pipeline testing, demonstrating its feasibility. The mean squared error of wall thickness reconstruction was 0.0070, achieving a high level of precision.

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“…The method in this paper is based on two widely recognized and rapidly developing technologies: ultrasonic guided wave detection technology and deep learning method. Ultrasonic guided wave can realize long-distance detection of the pipeline through a detection point, and can achieve full coverage of the tested area, while realizing the detection of the external surface, internal surface and internal defects of the pipe wall [9][10][11]. Compared with the above-mentioned small-area detection methods, the ultrasonic guided wave detection method greatly improves the detection efficiency.…”

Section: Introductionmentioning

confidence: 99%

Crack orientation recognition method based on prototype learning

Liu

Chen

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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To address the problem that the direction of pipe cracks is difficult to detect, a crack direction recognition method based on prototype learning is proposed with a prototype network as a framework. First, through the convolutional layer of the prototype network, shallow features of crack directions are extracted to improve the generalization ability of the model on the data set of this paper. then, by improving the High-Resolution Network and introducing a location self-attention mechanism, and combined with a migration training method for the data set of this paper, a category that can accurately reflect the crack directions is constructed prototype learning mechanism. Finally, pattern recognition is performed by the metric classification methods, the effective classification of crack direction under small sample condition is achieved. The experimental results show that the recognition accuracy of the crack direction recognition method based on prototype learning can reach 99.2% with the sample parameters unchanged.

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Novel design of an effective pneumatic magnetostrictive patch transducer based on the ultrasonic guided wave for application of fast pipe health inspection

Cited by 7 publications

References 23 publications

Performance investigation on the balance of static and dynamic magnetic field strength of magnetostrictive patch transducer with different permanent magnets

Performance investigation on the balance of static and dynamic magnetic field strength of magnetostrictive patch transducer with different permanent magnets

Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network

Crack orientation recognition method based on prototype learning

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